The present system is related to Object-centric Fine-grained Image Classification.
Significant progress has been made in image classification using deep convolutional neural networks (CNN). However, progress on the use of deep CNN for fine-grained image classification has been hampered by the lack of large-scale training data to avoid over-fitting fine-grained image labels. In fact, most existing fine-grained image classification benchmark dataset often consist of only a few tens of thousands of images. For example, while DCNN suffers from over-fitting on small datasets, most existing fine-grained classification benchmark datasets are fairly small because fine-grained class labels are hard to obtain, e.g., it is difficult to use Mechanical Turk for the labeling task due to lack of deep domain knowledge.